eric-xw
commented
4 years ago Hi, can you try calling contiguous() for the inputs before feeding them into the decoder LSTM?
The code is working on our end, so we cannot debug it.
Open LinuxBeginner opened 4 years ago
eric-xw
commented
4 years ago Hi, can you try calling contiguous() for the inputs before feeding them into the decoder LSTM?
The code is working on our end, so we cannot debug it.
LinuxBeginner
commented
4 years ago Hi eric, contiguous() is already implemented at line 169-173 in model.py
src_out_i = src_out[i].unsqueeze(0).expand(beam_size, src_out.size(1), src_out.size(2)).contiguous() # (bs, seq_len, N)
vid_out_i = vid_out[i].unsqueeze(0).expand(beam_size, vid_out.size(1), vid_out.size(2)).contiguous()
src_mask_i = src_mask[i].unsqueeze(0).expand(beam_size, src_mask.size(1)).contiguous()
hidden_i = [_[:, i, :].unsqueeze(1).expand(_.size(0), beam_size, _.size(2)).contiguous() for _ in
hidden] # (n_layers, bs, 1024)But, it is still not working, there was no issue at the time of training. The issue is showing only on running the eval.py
Please advice.
eric-xw
commented
4 years ago Reading the error log, the issue is when calling the LSTM in Line 110. So try calling contiguous() for rnn_input, last_hidden.
bozhenhhu
commented
4 years ago before this line output, hidden_i, attn_weights = self.onestep(output, hidden_i, src_out_i, vid_out_i, src_mask_i), I add .contiguous() after output and hidden_i as follows:
output = torch.from_numpy(outputs).cuda().contiguous()
def from_numpy(self, states):
return [torch.from_numpy(state).cuda().contiguous() for state in states]
it works.
Apart from this, I find the code in beam_decoding is very hard for me to figure out. It is hugly different with the code in inference, which I thought they may be similar before.
The second output, hidden_i, attn_weights = self.onestep(output, hidden_i, src_out_i, vid_out_i, src_mask_i)
may can be deleted.
hynbjn
commented
1 year ago @bozhenhhu I've tried the method you've suggested, but the code still does not work:( `import math import torch import random import numpy as np from torch import nn import torch.nn.functional as F
from utils import sos_idx, eos_idx
class SoftDotAttention(nn.Module): def init(self, dim_ctx, dim_h): '''Initialize layer.''' super(SoftDotAttention, self).init() self.linear_in = nn.Linear(dim_h, dim_ctx, bias=False) self.sm = nn.Softmax(dim=1)
def forward(self, context, h, mask=None):
'''Propagate h through the network.
h: batch x dim
context: batch x seq_len x dim
mask: batch x seq_len indices to be masked
'''
target = self.linear_in(h).unsqueeze(2) # batch x dim x 1
# Get attention
attn = torch.bmm(context, target).squeeze(2) # batch x seq_len
if mask is not None:
# -Inf masking prior to the softmax
attn.data.masked_fill_(mask, -float('inf'))
attn = self.sm(attn)
attn3 = attn.view(attn.size(0), 1, attn.size(1)) # batch x 1 x seq_len
weighted_ctx = torch.bmm(attn3, context) # batch x dim
return weighted_ctx, attnclass Encoder(nn.Module): def init(self, vocab_size, embed_size, hidden_size, n_layers=2, dropout=0.5): super(Encoder, self).init() self.hidden_size = hidden_size self.embed_size = embed_size self.src_embed = nn.Embedding(vocab_size, embed_size) self.src_encoder = nn.LSTM(input_size=embed_size, hidden_size=hidden_size // 2, num_layers=n_layers, dropout=dropout, batch_first=True, bidirectional=True)
self.frame_embed = nn.Linear(1024, self.embed_size)
self.video_encoder = nn.LSTM(input_size=embed_size, hidden_size=hidden_size // 2, num_layers=n_layers,
dropout=dropout, batch_first=True, bidirectional=True)
self.dropout = nn.Dropout(dropout, inplace=True)
def forward(self, src, vid, src_hidden=None, vid_hidden=None):
batch_size = src.size(0)
src_embedded = self.src_embed(src)
src_out, src_states = self.src_encoder(src_embedded, src_hidden)
src_h = src_states[0].permute(1, 0, 2).contiguous().view(
batch_size, 2, -1).permute(1, 0, 2)
src_c = src_states[1].permute(1, 0, 2).contiguous().view(
batch_size, 2, -1).permute(1, 0, 2)
vid_embedded = self.frame_embed(vid)
vid_out, vid_states = self.video_encoder(vid_embedded, vid_hidden)
vid_h = vid_states[0].permute(1, 0, 2).contiguous().view(
batch_size, 2, -1).permute(1, 0, 2)
vid_c = vid_states[0].permute(1, 0, 2).contiguous().view(
batch_size, 2, -1).permute(1, 0, 2)
init_h = torch.cat((src_h, vid_h), 2)
init_c = torch.cat((src_c, vid_c), 2)
return src_out, (init_h, init_c), vid_outclass Decoder(nn.Module): def init(self, embed_size, hidden_size, vocab_size, n_layers=2, dropout=0.5): super(Decoder, self).init() self.embed_size = embed_size self.hidden_size = hidden_size self.n_layers = n_layers self.vocab_size = vocab_size
self.embed = nn.Embedding(vocab_size, embed_size)
self.dropout = nn.Dropout(dropout, inplace=True)
self.src_attention = SoftDotAttention(embed_size, hidden_size)
self.vid_attention = SoftDotAttention(embed_size, hidden_size)
self.decoder = nn.LSTM(embed_size*3, hidden_size,
n_layers, dropout=dropout, batch_first=True)
self.fc = nn.Sequential(nn.Linear(self.hidden_size, self.embed_size),
nn.Tanh(),
nn.Dropout(p=dropout),
nn.Linear(embed_size, vocab_size))
def onestep(self, input, last_hidden, src_out, vid_out, src_mask):
'''
input: (B,)
'''
# Get the embedding of the current input word (last output word)
embedded = self.embed(input).unsqueeze(1) # (B,1, N)
embedded = self.dropout(embedded)
# Calculate attention weights and apply to encoder outputs
src_ctx, src_attn = self.src_attention(src_out, last_hidden[0][0], mask=src_mask) # src_ctx: (mb, 1, dim) attn: (mb, 1, seqlen)
vid_ctx, vid_attn = self.vid_attention(vid_out, last_hidden[0][0])
# Combine embedded input word and attended context, run through RNN
rnn_input = torch.cat([embedded, src_ctx, vid_ctx], 2) # (mb, 1, input_size)
output, hidden = self.decoder(rnn_input, last_hidden)
output = output.squeeze(1) # (B, 1, N) -> (B,N)
output = self.fc(output)
return output, hidden, (src_attn, vid_attn)
def forward(self, src, trg, init_hidden, src_out, vid_out, max_len, teacher_forcing_ratio):
batch_size = trg.size(0)
src_mask = (src == 0) # mask paddings.
outputs = torch.zeros(batch_size, max_len, self.vocab_size).cuda()
hidden = (init_hidden[0][:self.n_layers].contiguous(), init_hidden[1][:self.n_layers].contiguous())
output = trg.data[:, 0] # <sos>
for t in range(1, max_len):
output, hidden, attn_weights = self.onestep(output, hidden, src_out, vid_out, src_mask) # (mb, vocab) (1, mb, N) (mb, 1, seqlen)
outputs[:, t, :] = output
is_teacher = random.random() < teacher_forcing_ratio
top1 = output.data.max(1)[1]
output = (trg.data[:, t] if is_teacher else top1).cuda() # output should be indices to feed into nn.embedding at next step
return outputs
def inference(self, src, trg, init_hidden, src_out, vid_out, max_len, teacher_forcing_ratio=0):
'''
Greedy decoding
'''
batch_size = trg.size(0)
src_mask = (src == 0) # mask paddings.
outputs = torch.zeros(batch_size, max_len, self.vocab_size).cuda()
hidden = (init_hidden[0][:self.n_layers].contiguous(), init_hidden[1][:self.n_layers].contiguous())
output = trg.data[:, 0] # <sos>
pred_lengths = [0]*batch_size
for t in range(1, max_len):
output, hidden, attn_weights = self.onestep(output, hidden, src_out, vid_out, src_mask) # (mb, vocab) (1, mb, N) (mb, 1, seqlen)
outputs[:, t, :] = output
is_teacher = random.random() < teacher_forcing_ratio
top1 = output.data.max(1)[1]
output = (trg.data[:, t] if is_teacher else top1).cuda()
for i in range(batch_size):
if output[i]==3 and pred_lengths[i]==0:
pred_lengths[i] = t
for i in range(batch_size):
if pred_lengths[i]==0:
pred_lengths[i] = max_len
return outputs, pred_lengths
def beam_decoding(self, src, init_hidden, src_out, vid_out, max_len, beam_size=5):
batch_size = src.size(0)
src_mask = (src == 0) # mask padding
hidden = (init_hidden[0][:self.n_layers].contiguous(), init_hidden[1][:self.n_layers].contiguous())
seq = torch.LongTensor(max_len, batch_size).zero_()
seq_log_probs = torch.FloatTensor(max_len, batch_size)
for i in range(batch_size):
# treat the problem as having a batch size of beam_size
src_out_i = src_out[i].unsqueeze(0).expand(beam_size, src_out.size(1), src_out.size(2)).contiguous() # (bs, seq_len, N)
vid_out_i = vid_out[i].unsqueeze(0).expand(beam_size, vid_out.size(1), vid_out.size(2)).contiguous()
src_mask_i = src_mask[i].unsqueeze(0).expand(beam_size, src_mask.size(1)).contiguous()
hidden_i = [_[:, i, :].unsqueeze(1).expand(_.size(0), beam_size, _.size(2)).contiguous() for _ in
hidden] # (n_layers, bs, 1024)
output = torch.LongTensor([sos_idx] * beam_size).cuda()
output, hidden_i, attn_weights = self.onestep(output, hidden_i, src_out_i, vid_out_i, src_mask_i) # (mb, vocab) (1, mb, N) (mb, 1, seqlen)
log_probs = F.log_softmax(output, dim=1)
log_probs[:, -1] = log_probs[:, -1] - 1000
neg_log_probs = -log_probs
all_outputs = np.ones((1, beam_size), dtype='int32')
all_masks = np.ones_like(all_outputs, dtype="float32")
all_costs = np.zeros_like(all_outputs, dtype="float32")
for j in range(max_len):
if all_masks[-1].sum() == 0:
break
next_costs = (
all_costs[-1, :, None] + neg_log_probs.data.cpu().numpy() * all_masks[-1, :, None])
(finished,) = np.where(all_masks[-1] == 0)
next_costs[finished, 1:] = np.inf
(indexes, outputs), chosen_costs = self._smallest(
next_costs, beam_size, only_first_row=j == 0)
new_state_d = [_.data.cpu().numpy()[:, indexes, :]
for _ in hidden_i]
all_outputs = all_outputs[:, indexes]
all_masks = all_masks[:, indexes]
all_costs = all_costs[:, indexes]
output = torch.from_numpy(outputs).cuda().contiguous()
hidden_i = self.from_numpy(new_state_d)
output, hidden_i, attn_weights = self.onestep(output, hidden_i, src_out_i, vid_out_i, src_mask_i)
log_probs = F.log_softmax(output, dim=1)
log_probs[:, -1] = log_probs[:, -1] - 1000
neg_log_probs = -log_probs
all_outputs = np.vstack([all_outputs, outputs[None, :]])
all_costs = np.vstack([all_costs, chosen_costs[None, :]])
mask = outputs != 0
all_masks = np.vstack([all_masks, mask[None, :]])
all_outputs = all_outputs[1:]
all_costs = all_costs[1:] - all_costs[:-1]
all_masks = all_masks[:-1]
costs = all_costs.sum(axis=0)
lengths = all_masks.sum(axis=0)
normalized_cost = costs / lengths
best_idx = np.argmin(normalized_cost)
seq[:all_outputs.shape[0], i] = torch.from_numpy(
all_outputs[:, best_idx])
seq_log_probs[:all_costs.shape[0], i] = torch.from_numpy(
all_costs[:, best_idx])
seq, seq_log_probs = seq.transpose(0, 1), seq_log_probs.transpose(0, 1)
pred_lengths = [0]*batch_size
for i in range(batch_size):
if sum(seq[i] == eos_idx) == 0:
pred_lengths[i] = max_len
else:
pred_lengths[i] = (seq[i] == eos_idx).nonzero()[0][0]
# return the samples and their log likelihoods
return seq, pred_lengths # seq_log_probs
def from_numpy(self, states):
return [torch.from_numpy(state).cuda().contiguous() for state in states]
@staticmethod
def _smallest(matrix, k, only_first_row=False):
if only_first_row:
flatten = matrix[:1, :].flatten()
else:
flatten = matrix.flatten()
args = np.argpartition(flatten, k)[:k]
args = args[np.argsort(flatten[args])]
return np.unravel_index(args, matrix.shape), flatten[args]` This is the code I ran. What did I do wrong?
bozhenhhu
commented
1 year ago @bozhenhhu I've tried the method you've suggested, but the code still does not work:( `import math import torch import random import numpy as np from torch import nn import torch.nn.functional as F
from utils import sos_idx, eos_idx
class SoftDotAttention(nn.Module): def init(self, dim_ctx, dim_h): '''Initialize layer.''' super(SoftDotAttention, self).init() self.linear_in = nn.Linear(dim_h, dim_ctx, bias=False) self.sm = nn.Softmax(dim=1)
def forward(self, context, h, mask=None): '''Propagate h through the network. h: batch x dim context: batch x seq_len x dim mask: batch x seq_len indices to be masked ''' target = self.linear_in(h).unsqueeze(2) # batch x dim x 1 # Get attention attn = torch.bmm(context, target).squeeze(2) # batch x seq_len if mask is not None: # -Inf masking prior to the softmax attn.data.masked_fill_(mask, -float('inf')) attn = self.sm(attn) attn3 = attn.view(attn.size(0), 1, attn.size(1)) # batch x 1 x seq_len weighted_ctx = torch.bmm(attn3, context) # batch x dim return weighted_ctx, attnclass Encoder(nn.Module): def init(self, vocab_size, embed_size, hidden_size, n_layers=2, dropout=0.5): super(Encoder, self).init() self.hidden_size = hidden_size self.embed_size = embed_size self.src_embed = nn.Embedding(vocab_size, embed_size) self.src_encoder = nn.LSTM(input_size=embed_size, hidden_size=hidden_size // 2, num_layers=n_layers, dropout=dropout, batch_first=True, bidirectional=True)
self.frame_embed = nn.Linear(1024, self.embed_size) self.video_encoder = nn.LSTM(input_size=embed_size, hidden_size=hidden_size // 2, num_layers=n_layers, dropout=dropout, batch_first=True, bidirectional=True) self.dropout = nn.Dropout(dropout, inplace=True) def forward(self, src, vid, src_hidden=None, vid_hidden=None): batch_size = src.size(0) src_embedded = self.src_embed(src) src_out, src_states = self.src_encoder(src_embedded, src_hidden) src_h = src_states[0].permute(1, 0, 2).contiguous().view( batch_size, 2, -1).permute(1, 0, 2) src_c = src_states[1].permute(1, 0, 2).contiguous().view( batch_size, 2, -1).permute(1, 0, 2) vid_embedded = self.frame_embed(vid) vid_out, vid_states = self.video_encoder(vid_embedded, vid_hidden) vid_h = vid_states[0].permute(1, 0, 2).contiguous().view( batch_size, 2, -1).permute(1, 0, 2) vid_c = vid_states[0].permute(1, 0, 2).contiguous().view( batch_size, 2, -1).permute(1, 0, 2) init_h = torch.cat((src_h, vid_h), 2) init_c = torch.cat((src_c, vid_c), 2) return src_out, (init_h, init_c), vid_outclass Decoder(nn.Module): def init(self, embed_size, hidden_size, vocab_size, n_layers=2, dropout=0.5): super(Decoder, self).init() self.embed_size = embed_size self.hidden_size = hidden_size self.n_layers = n_layers self.vocab_size = vocab_size
self.embed = nn.Embedding(vocab_size, embed_size) self.dropout = nn.Dropout(dropout, inplace=True) self.src_attention = SoftDotAttention(embed_size, hidden_size) self.vid_attention = SoftDotAttention(embed_size, hidden_size) self.decoder = nn.LSTM(embed_size*3, hidden_size, n_layers, dropout=dropout, batch_first=True) self.fc = nn.Sequential(nn.Linear(self.hidden_size, self.embed_size), nn.Tanh(), nn.Dropout(p=dropout), nn.Linear(embed_size, vocab_size)) def onestep(self, input, last_hidden, src_out, vid_out, src_mask): ''' input: (B,) ''' # Get the embedding of the current input word (last output word) embedded = self.embed(input).unsqueeze(1) # (B,1, N) embedded = self.dropout(embedded) # Calculate attention weights and apply to encoder outputs src_ctx, src_attn = self.src_attention(src_out, last_hidden[0][0], mask=src_mask) # src_ctx: (mb, 1, dim) attn: (mb, 1, seqlen) vid_ctx, vid_attn = self.vid_attention(vid_out, last_hidden[0][0]) # Combine embedded input word and attended context, run through RNN rnn_input = torch.cat([embedded, src_ctx, vid_ctx], 2) # (mb, 1, input_size) output, hidden = self.decoder(rnn_input, last_hidden) output = output.squeeze(1) # (B, 1, N) -> (B,N) output = self.fc(output) return output, hidden, (src_attn, vid_attn) def forward(self, src, trg, init_hidden, src_out, vid_out, max_len, teacher_forcing_ratio): batch_size = trg.size(0) src_mask = (src == 0) # mask paddings. outputs = torch.zeros(batch_size, max_len, self.vocab_size).cuda() hidden = (init_hidden[0][:self.n_layers].contiguous(), init_hidden[1][:self.n_layers].contiguous()) output = trg.data[:, 0] # <sos> for t in range(1, max_len): output, hidden, attn_weights = self.onestep(output, hidden, src_out, vid_out, src_mask) # (mb, vocab) (1, mb, N) (mb, 1, seqlen) outputs[:, t, :] = output is_teacher = random.random() < teacher_forcing_ratio top1 = output.data.max(1)[1] output = (trg.data[:, t] if is_teacher else top1).cuda() # output should be indices to feed into nn.embedding at next step return outputs def inference(self, src, trg, init_hidden, src_out, vid_out, max_len, teacher_forcing_ratio=0): ''' Greedy decoding ''' batch_size = trg.size(0) src_mask = (src == 0) # mask paddings. outputs = torch.zeros(batch_size, max_len, self.vocab_size).cuda() hidden = (init_hidden[0][:self.n_layers].contiguous(), init_hidden[1][:self.n_layers].contiguous()) output = trg.data[:, 0] # <sos> pred_lengths = [0]*batch_size for t in range(1, max_len): output, hidden, attn_weights = self.onestep(output, hidden, src_out, vid_out, src_mask) # (mb, vocab) (1, mb, N) (mb, 1, seqlen) outputs[:, t, :] = output is_teacher = random.random() < teacher_forcing_ratio top1 = output.data.max(1)[1] output = (trg.data[:, t] if is_teacher else top1).cuda() for i in range(batch_size): if output[i]==3 and pred_lengths[i]==0: pred_lengths[i] = t for i in range(batch_size): if pred_lengths[i]==0: pred_lengths[i] = max_len return outputs, pred_lengths def beam_decoding(self, src, init_hidden, src_out, vid_out, max_len, beam_size=5): batch_size = src.size(0) src_mask = (src == 0) # mask padding hidden = (init_hidden[0][:self.n_layers].contiguous(), init_hidden[1][:self.n_layers].contiguous()) seq = torch.LongTensor(max_len, batch_size).zero_() seq_log_probs = torch.FloatTensor(max_len, batch_size) for i in range(batch_size): # treat the problem as having a batch size of beam_size src_out_i = src_out[i].unsqueeze(0).expand(beam_size, src_out.size(1), src_out.size(2)).contiguous() # (bs, seq_len, N) vid_out_i = vid_out[i].unsqueeze(0).expand(beam_size, vid_out.size(1), vid_out.size(2)).contiguous() src_mask_i = src_mask[i].unsqueeze(0).expand(beam_size, src_mask.size(1)).contiguous() hidden_i = [_[:, i, :].unsqueeze(1).expand(_.size(0), beam_size, _.size(2)).contiguous() for _ in hidden] # (n_layers, bs, 1024) output = torch.LongTensor([sos_idx] * beam_size).cuda() output, hidden_i, attn_weights = self.onestep(output, hidden_i, src_out_i, vid_out_i, src_mask_i) # (mb, vocab) (1, mb, N) (mb, 1, seqlen) log_probs = F.log_softmax(output, dim=1) log_probs[:, -1] = log_probs[:, -1] - 1000 neg_log_probs = -log_probs all_outputs = np.ones((1, beam_size), dtype='int32') all_masks = np.ones_like(all_outputs, dtype="float32") all_costs = np.zeros_like(all_outputs, dtype="float32") for j in range(max_len): if all_masks[-1].sum() == 0: break next_costs = ( all_costs[-1, :, None] + neg_log_probs.data.cpu().numpy() * all_masks[-1, :, None]) (finished,) = np.where(all_masks[-1] == 0) next_costs[finished, 1:] = np.inf (indexes, outputs), chosen_costs = self._smallest( next_costs, beam_size, only_first_row=j == 0) new_state_d = [_.data.cpu().numpy()[:, indexes, :] for _ in hidden_i] all_outputs = all_outputs[:, indexes] all_masks = all_masks[:, indexes] all_costs = all_costs[:, indexes] output = torch.from_numpy(outputs).cuda().contiguous() hidden_i = self.from_numpy(new_state_d) output, hidden_i, attn_weights = self.onestep(output, hidden_i, src_out_i, vid_out_i, src_mask_i) log_probs = F.log_softmax(output, dim=1) log_probs[:, -1] = log_probs[:, -1] - 1000 neg_log_probs = -log_probs all_outputs = np.vstack([all_outputs, outputs[None, :]]) all_costs = np.vstack([all_costs, chosen_costs[None, :]]) mask = outputs != 0 all_masks = np.vstack([all_masks, mask[None, :]]) all_outputs = all_outputs[1:] all_costs = all_costs[1:] - all_costs[:-1] all_masks = all_masks[:-1] costs = all_costs.sum(axis=0) lengths = all_masks.sum(axis=0) normalized_cost = costs / lengths best_idx = np.argmin(normalized_cost) seq[:all_outputs.shape[0], i] = torch.from_numpy( all_outputs[:, best_idx]) seq_log_probs[:all_costs.shape[0], i] = torch.from_numpy( all_costs[:, best_idx]) seq, seq_log_probs = seq.transpose(0, 1), seq_log_probs.transpose(0, 1) pred_lengths = [0]*batch_size for i in range(batch_size): if sum(seq[i] == eos_idx) == 0: pred_lengths[i] = max_len else: pred_lengths[i] = (seq[i] == eos_idx).nonzero()[0][0] # return the samples and their log likelihoods return seq, pred_lengths # seq_log_probs def from_numpy(self, states): return [torch.from_numpy(state).cuda().contiguous() for state in states] @staticmethod def _smallest(matrix, k, only_first_row=False): if only_first_row: flatten = matrix[:1, :].flatten() else: flatten = matrix.flatten() args = np.argpartition(flatten, k)[:k] args = args[np.argsort(flatten[args])] return np.unravel_index(args, matrix.shape), flatten[args]` This is the code I ran. What did I do wrong?
Do you have the same environment as this repository, like the prerequisites? It has been a long time since this model was published, and many packages have been updated, which may result in incompatibility. Why not try the up-to-date methods?
Training was successful. Data: vatex_training_v1.0.json vatex_validation_v1.0.json vatex_public_test_english_v1.1.json
System: Google Colab GPU
When I tried to run the
python eval.py, it is showing the following errorVocab size src/tgt:10523/2907 train/val/test size: 254/30/59 **** Start eval... **** Use epoch 34 as the best model for testing Traceback (most recent call last): File "eval.py", line 123, in
main(args)
File "eval.py", line 63, in main
eval(test_loader, encoder, decoder, cp_file, tok_tgt, result_path)
File "eval.py", line 90, in eval
preds, pred_lengths = decoder.beam_decoding(srccap, init_hidden, src_out, vid_out, args.MAX_INPUT_LENGTH, beam_size=5)
File "/content/drive/My Drive/MMT/MMTvatex/Video-guided-Machine-Translation/model.py", line 208, in beam_decoding
output, hidden_i, attn_weights = self.onestep(output, hidden_i, src_out_i, vid_out_i, src_mask_i)
File "/content/drive/My Drive/MMT/MMTvatex/Video-guided-Machine-Translation/model.py", line 110, in onestep
output, hidden = self.decoder(rnn_input, last_hidden)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 550, in call
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py", line 570, in forward
self.dropout, self.training, self.bidirectional, self.batch_first)
RuntimeError: rnn: hx is not contiguous
Could you please tell me why is this happening? Thank you.